In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

In this paper, we have shown that both human and machine translation can alter superficial patterns in data, which requires reconsidering previous findings in cross-lingual transfer learning. 
 Question: Does the professional translation or the machine translation introduce the artifacts?
Yes

We extract user's age by applying regular expression patterns to profile descriptions (such as "17 years old, self-harm, anxiety, depression") BIBREF41 . We compile "age prefixes" and "age suffixes", and use three age-extraction rules: 1. I am X years old 2. Born in X 3. X years old, where X is a "date" or age (e.g., 1994). We selected a subset of 1464 users INLINEFORM0 from INLINEFORM1 who disclose their gender in their profile description. 
 Question: Where does the information on individual-level demographics come from?
From Twitter profile descriptions of the users.

The following handcrafted features were used for the model:

Bias feature

Token feature

Uppercase feature (y/n)

Titlecase feature (y/n)

Character trigram feature

Quotation feature (y/n)

Word suffix feature (last three characters)

POS tag (provided by spaCy utilities)

Word shape (provided by spaCy utilities)

Word embedding (see Table TABREF26) 
 Question: What are the handcrafted features used?

Bias feature Token feature Uppercase feature (y/n) Titlecase feature (y/n) Character trigram feature Quotation feature (y/n) Word suffix feature (last three characters) POS tag (provided by spaCy utilities) Word shape (provided by spaCy utilities) Word embedding (see Table TABREF26)

